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A Simple Adaptive Unfolding Network for Hyperspectral Image Reconstruction

2023-01-24 18:28:21
Junyu Wang, Shijie Wang, Wenyu Liu, Zengqiang Zheng, Xinggang Wang

Abstract

We present a simple, efficient, and scalable unfolding network, SAUNet, to simplify the network design with an adaptive alternate optimization framework for hyperspectral image (HSI) reconstruction. SAUNet customizes a Residual Adaptive ADMM Framework (R2ADMM) to connect each stage of the network via a group of learnable parameters to promote the usage of mask prior, which greatly stabilizes training and solves the accuracy degradation issue. Additionally, we introduce a simple convolutional modulation block (CMB), which leads to efficient training, easy scale-up, and less computation. Coupling these two designs, SAUNet can be scaled to non-trivial 13 stages with continuous improvement. Without bells and whistles, SAUNet improves both performance and speed compared with the previous state-of-the-art counterparts, which makes it feasible for practical high-resolution HSI reconstruction scenarios. We set new records on CAVE and KAIST HSI reconstruction benchmarks. Code and models are available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2301.10208

PDF

https://arxiv.org/pdf/2301.10208


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